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The goal of this review is to bring together material from cognitive psychology with recent machine vision studies to identify plausible neural mechanisms for visual same-different discrimination and relational understanding. We highlight how developments in the study of artificial neural networks provide computational evidence implicating attention and working memory in the ascertaining of visual relations, including same- different relations. We review some recent attempts to incorporate these mechanisms into flexible models of visual reasoning. Particular attention is given to recent models jointly trained on visual and linguistic information. These recent systems are promising, but they still fall short of the biological standard in several ways, which we outline in a final section.
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Emergence of relational reasoning
We review recent theoretical and empirical work on the emergence of relational reasoning, drawing connections among the fields of comparative psychology, developmental psychology, cognitive neuroscience, cognitive science, and machine learning. Relational learning appears to involve multiple systems: a suite of Early Systems that are available to human infants and are shared to some extent with nonhuman animals; and a Late System that emerges in humans only, at approximately age three years. The Late System supports reasoning with explicit role-governed relations, and is closely tied to the functions of a frontoparietal network in the human brain. Recent work in cognitive science and machine learning suggests that humans (and perhaps machines) may acquire abstract relations from nonrelational inputs by means of processes that enable re-representation.
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- Award ID(s):
- 1827374
- PAR ID:
- 10231808
- Date Published:
- Journal Name:
- Current opinion in behavioral sciences
- Volume:
- 37
- ISSN:
- 2352-1554
- Page Range / eLocation ID:
- 118-124
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/doi.org/10.1016/j.cobeha.2020.11.012
